Real-Time Processor-in-Loop Investigation of a Modified Non-Linear State Observer Using Sliding Modes for Speed Sensorless Induction Motor Drive in Electric Vehicles

Abstract

Tracking performance and stability play a major role in observer design for speed estimation purpose in motor drives used in vehicles. It is all the more prevalent at lower speed ranges. There was a need to have a tradeoff between these parameters ensuring the speed bandwidth remains as wide as possible. This work demonstrates an improved static and dynamic performance of a sliding mode state observer used for speed sensorless 3 phase induction motor drive employed in electric vehicles (EVs). The estimated torque is treated as a model disturbance and integrated into the state observer while the error is constrained in the sliding hyperplane. Two state observers with different disturbance handling mechanisms have been designed. Depending on, how they reject disturbances, based on their structure, their performance is studied and analyzed with respect to speed bandwidth, tracking and disturbance handling capability. The proposed observer with superior disturbance handling capabilities is able to provide a wider speed range, which is a main issue in EV. Here, a new dimension of model based design strategy is employed namely the Processor-in-Loop. The concept is validated in a real-time model based design test bench powered by RT-lab. The plant and the controller are built in a Simulink environment and made compatible with real-time blocksets and the system is executed in real-time targets OP4500/OP5600 (Opal-RT). Additionally, the Processor-in-Loop hardware verification is performed by using two adapters, which are used to loop-back analog and digital input and outputs. It is done to include a real-world signal routing between the plant and the controller thereby, ensuring a real-time interaction between the plant and the controller. Results validated portray better disturbance handling, steady state and a dynamic tracking profile, higher speed bandwidth and lesser torque pulsations compared to the conventional observer.

A Modified SOGI-PLL with Adjustable Refiltering for Improved Stability and Reduced Response Time

“…In this section, several verification tests are carried out in order to verify the performance of the proposed ARF-SOGI-PLL. The tests are made with co-simulations implemented under the processor-in-the-loop (PIL) [33,34] approach in MATLAB/Simulink with Code Composer Studio (CCS) and the digital signal controller TMS320F28335 from Texas Instruments.…”

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

“…Additionally, additive noise emulates fluctuations and disturbances due to transduction and other physical phenomena not considered in the model. Therefore, the motor supply voltage vector $$V^{motor}_{abc}$$ is a function of the control voltage $$V^{control}_{abc}$$, the switching period $$T_{sw}$$, and the noise signal $$N_{\mu, \sigma }(t)$$ [58]. $$$$\begin{equation} V^{motor}_{abc}(t) = V^{control}_{abc} {\left(t - T_{sw} \right)} + N_{\mu, \sigma }(t), \end{equation}$$$$…”

“…We assume that the inverter's efficiency is (almost) unity regarding power flow, accounting for losses through an algebraic model of losses on the active components. This assumption is widely used in designing control and monitoring algorithms for electric drives, allowing for reduced simulation times up to real-time throughput, crucial for monitoring purposes [58][59][60].…”

Design and Xilinx Virtex-field-programmable gate array for hardware in the loop of sensorless second-order sliding mode control and model reference adaptive system–sliding mode observer for direct torque control of induction motor drive

“…38,39 These problems make sensorless control more attractive relative to the latter based on the speed sensor. Various estimation techniques for the rotor speed have been suggested in the literature in order to estimate the rotor speed in a closed loop utilizing the measured stator currents and voltages, like the full-order observer, 40 the extended Kalman filter, [41][42][43] the SMO, [44][45][46][47][48] the Luenberger observer 49,50 and the model reference adaptive system (MRAS). [51][52][53][54][55][56][57][58][59][60][61] Mostly, sensorless algorithms have proved their good performance at high-and mediumspeed ranges.…”

“…It can be chosen thanks to its ease of implementation, good robustness under disturbance and parameter uncertainties, and low calculation burden requirements. [44][45][46][47][48] Some structures of SMOs are developed 44,46 when the rotor speed is defined as an adaptive quantity. In these observer structures, the rotor speed is estimated through the observation errors, which causes a delay in the estimation loop.…”